Abstract
The international spread of Zika virus (ZIKV) began in Brazil in 2015. To estimate the risk of observing imported ZIKV cases, we calculated effective distance, typically an excellent predictor of arrival time, from airline network data. However, we eventually concluded that, for ZIKV, effective distance alone is not an adequate predictor of arrival time, which we partly attributed to the difficulty of diagnosing and ascertaining ZIKV infections. Herein, we explored the mechanisms behind the observed time delay of ZIKV importation by country, statistically decomposing the delay into two parts: the actual time to importation from Brazil and the reporting delay. The latter was modeled as a function of the gross domestic product (GDP) and other variables that influence underlying diagnostic capacity in a given country. We showed that a high GDP per capita is a good predictor of short reporting delay. ZIKV infection is generally mild and, without substantial laboratory capacity, cases can be underestimated. This study successfully demonstrates this phenomenon and emphasizes the importance of accounting for reporting delays as part of the data generating process for estimating time to importation.
| Original language | English |
|---|---|
| Pages (from-to) | 3272-3284 |
| Number of pages | 13 |
| Journal | Mathematical Biosciences and Engineering |
| Volume | 16 |
| Issue number | 5 |
| DOIs | |
| State | Published - 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Effective distance
- Global spread
- Network
- Pandemic
- Prediction
- Probabilistic models
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